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IJCI. International Journal of Computers and Information
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Elsharkawy, B., Ahmed, H., Salem, R. (2016). Semantic-based Approach for Solving the Heterogeneity of Clinical Data. IJCI. International Journal of Computers and Information, 5(1), 35-45. doi: 10.21608/ijci.2016.33955
Basma Elsharkawy; Hatem Ahmed; Rashed Salem. "Semantic-based Approach for Solving the Heterogeneity of Clinical Data". IJCI. International Journal of Computers and Information, 5, 1, 2016, 35-45. doi: 10.21608/ijci.2016.33955
Elsharkawy, B., Ahmed, H., Salem, R. (2016). 'Semantic-based Approach for Solving the Heterogeneity of Clinical Data', IJCI. International Journal of Computers and Information, 5(1), pp. 35-45. doi: 10.21608/ijci.2016.33955
Elsharkawy, B., Ahmed, H., Salem, R. Semantic-based Approach for Solving the Heterogeneity of Clinical Data. IJCI. International Journal of Computers and Information, 2016; 5(1): 35-45. doi: 10.21608/ijci.2016.33955

Semantic-based Approach for Solving the Heterogeneity of Clinical Data

Article 4, Volume 5, Issue 1, Spring 2016, Page 35-45  XML PDF (714.85 K)
Document Type: Original Article
DOI: 10.21608/ijci.2016.33955
Authors
Basma Elsharkawy* 1; Hatem Ahmed2; Rashed Salem1
1Faculty of Computers and Information, Menoufia University, Shebin Elkom, Egypt.
2Faculty of Computer and Information Menoufia University
Abstract
Clinical records contain massive heterogeneity number of data types, generally written in free-note without a linguistic standard. Other forms of medical data include medical images with/without metadata (e.g., CT, MRI, radiology, etc.), audios (e.g., transcriptions, ultrasound), videos (e.g., surgery recording), and structured data (e.g., laboratory test
results, age, year, weight, billing, etc.). Consequently, to retrieve the knowledge from these data is not trivial task.
Handling the heterogeneity besides largeness and complexity of these data is a challenge. The main purpose of this paper
is proposing a framework with two-fold. Firstly, it achieves a semantic-based integration approach, which resolves the
heterogeneity issue during the integration process of healthcare data from various data sources. Secondly, it achieves a
semantic-based medical retrieval approach with enhanced precision. Our experimental study on medical datasets
demonstrates the significant accuracy and speedup of the proposed framework over existing approaches.

Keywords
Schema data integration; Heterogeneity; Image retrieval; Semantic ontology; OWL; RDF; XML
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